Depot Vehicle Target Identification Using 3D Object Depth and IDs
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Solution Overview
Problem
Existing vehicle control systems struggle to efficiently and accurately identify and navigate to assigned objects, such as ramps or trailers, within a depot environment without centralized specification, often requiring high computing power and leading to inaccuracies in 2D image recognition and time-consuming manual identification processes.
Innovation Solution
A method involving a vehicle's environment detection system, using mono or stereo cameras, LIDAR, time-of-flight cameras, or structured-light cameras, to detect and classify three-dimensional objects, identify them via object identifiers, and control the vehicle's approach using steering, braking, and drive systems based on depth information and object identifiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If machine learning using pattern recognition is used to identify assigned ramps or objects, then the vehicle can automatically identify targets, but high computing power and large databases are required
Solution Approach 1:
The patent segments the target identification process into two parts: (1) a centralized management system pre-processes and stores assignment information in a database, and (2) the vehicle's onboard system only performs simple database queries and image recognition. This segmentation reduces the computing burden on the vehicle while maintaining automation.
Solution Approach 2:
The centralized management system performs preliminary actions by pre-defining ramp assignments and storing them in a database before the vehicle needs to identify targets. This advance preparation eliminates the need for complex real-time computation during vehicle operation.
2Extent of automation
If machine learning based on images is used to identify objects, then automatic recognition is achieved, but inaccuracies in 2D image space reproduction occur
Solution Approach 1:
The patent transitions from 2D image-only recognition to a multi-dimensional approach by integrating 2D camera images with 3D depth information from LIDAR or time-of-flight cameras. This dimensional enhancement allows for more accurate spatial positioning and object identification.
Solution Approach 2:
The patent merges data from multiple sensor types (cameras, LIDAR, time-of-flight cameras) and combines them with database information from the centralized management system. This fusion of multiple information sources improves recognition accuracy and reduces errors.
3Device complexity
If a centralized management system defines paths and ramp assignments, then vehicle control is simplified, but the system requires centralized specification for every movement
Solution Approach 1:
The vehicle performs self-service by autonomously querying the centralized database for its assigned targets and independently navigating to them using its onboard sensors and control systems. This reduces the need for continuous centralized control while maintaining operational flexibility.
Solution Approach 2:
The centralized database acts as an intermediary that stores assignment information without requiring continuous active control. The vehicle interacts with this passive database to obtain target information, enabling flexible operations without constant centralized intervention.
4Productivity
If 20 ramps are in close proximity to each other, then depot operations are efficient, but finding an assigned ramp becomes time-consuming
Solution Approach 1:
The centralized management system performs preliminary assignment of specific ramps to vehicles and stores this information in a database before the vehicle arrives. This advance preparation allows the vehicle to immediately identify its assigned ramp without time-consuming search procedures.
Solution Approach 2:
The patent replaces manual visual search and mechanical identification methods with automated optical recognition systems (cameras) and electronic database queries. This substitution dramatically reduces the time required to identify assigned ramps among closely spaced options.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables efficient, automated, and accurate identification and navigation to assigned depot objects, reducing the need for centralized path specification and minimizing human intervention, while adapting to different environmental conditions.
Implementation Method 1
detecting a three-dimensional object in the environment of the vehicle and determining depth information for the detected three-dimensional object
Implementation Method 2
time-of-flight cameras, or structured-light cameras, to detect and classify three-dimensional objects
Implementation Method 3
mono or stereo cameras, LIDAR, time-of-flight cameras, or structured-light cameras, to detect and classify three-dimensional objects
Data Source
AI summary
A method is for controlling a vehicle in a depot. The method includes: allocating a three-dimensional target object to the vehicle; detecting a three-dimensional object in the environment around the vehicle and determining depth information for the detected three-dimensional object; classifying the detected three-dimensional object on the determined depth information and checking whether the determined three-dimensional object has the same object class as the three-dimensional target object; identifying the detected three-dimensional object if the determined three-dimensional object has the same object class as the three-dimensional target object by detecting an object identifier assigned to the three-dimensional object and checking whether the detected object identifier matches a target identifier assigned to the target object; outputting an approach signal to move the vehicle closer to the detected three-dimensional target object in an automated manner or manually if the object identifier matches the target identifier.